Modern precision agriculture is undermined by data scarcity, privacy concerns, and high latency in remote areas, and centralized AI models struggle to forecast rare, climate-induced crop stresses given insufficiently diverse datasets and stakeholders' reluctance to share sensitive data. To address these obstacles, this study presents GenFedEdgeAgri, a framework aimed at improving climate-resilient crop monitoring, soil intelligence, and intelligent irrigation, and it examines how synthetic, data-driven augmentation-achieved by incorporating Generative Diffusion Models (GDMs) into a Federated Edge AIoT architecture-can enhance model robustness while preserving strong localized privacy. Method... More >